Machine-learning potentials for aluminium were designed very recently
to describe essentially the properties of the solid states
[1]}, [2]} and the melting temperature
[1]}. Two different approaches were put forward
respectively with a Gaussian kernel regression [1]}
and a deep NN [2]} with a dataset built from
configurations extracted from AIMD simulations at various temperatures.
ML potentials were initially trained using the DFT energies starting
from the work of Behler and Parrinello on bulk Si [6]}.
It was subsequently pointed out that the learning process could
benefit from a wealth of additional information if the three
components of the force and six components of the stress per atom
are taken into account [7]}, [8]}, while one has
only a single energy value per simulated configuration. Still in
some works, only the forces have been used for the training showing
that properties like the vibrational properties in the solid states
can be reproduced, but they remain insufficient to get full account
of thermodynamic quantities [9]}, [1]}. Interestingly,
a very recent ML potential for bulk Si, shown to be able to unravel
the early stages of crystallization, was trained only using the
energies [11]}. Thus the question whether additional
information enhances the training or not is still an open issue,
given also the fact that the relative importance of the energy,
forces and stresses for estimating the Mean-Square Error (MSE) or
the Root-MSE (RMSE) introduce two additional free training parameters
[7]}. Moreover, the question of the transferability
of a ML potential taking into account both the liquid and solid
phases as mentioned above, remains essentially unexplored for
aluminium and is of importance when dealing with solidification
phenomena.
